Estimation Techniques for Common Cause Failure Data With Different System Sizes
作者:
PaulH. Kvam,
期刊:
Technometrics
(Taylor Available online 1996)
卷期:
Volume 38,
issue 4
页码: 382-388
ISSN:0040-1706
年代: 1996
DOI:10.1080/00401706.1996.10484550
出版商: Taylor & Francis Group
关键词: Aloha-factor model;Attribute data;Binomial failure-rate model;Mapping;Reliability
数据来源: Taylor
摘要:
Modeling of system lifetimes becomes more complicated when external events can cause the simultaneous failure of two or more system components. Models that ignore these common cause failures lead to methods of analysis that overestimate system reliability. Typical data consist of observed frequencies in whichiout ofm(identical) components in a system failed simultaneously,i= 1,…,m. Because this attribute data is inherently dependent on the number of components in the system, procedures for interpretation of data from different groups with more or fewer components than the system under study are not straightforward. This is a recurrent problem in reliability applications in which component configurations change from one system to the next. For instance, in the analysis of a large power-supply system that has three stand-by diesel generators in case of power loss, statistical tests and estimates of system reliability cannot be derived easily from data pertaining to different plants for which only one or two diesel generators were used to reinforce the main power source. This article presents, discusses, and analyzes methods to use generic attribute reliability data efficiently for systems of varying size.
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